Artificial Intelligence (AI) has rapidly transformed from being a field accessible only to technical specialists to an integral part of almost all industries today. Research from McKinsey shows that AI adoption is 2.5x higher today than it was in 2017 with capabilities being embedded in key areas such as robotics, computer vision, deep learning and natural language processing. Generative AI, in particular, has the potential to deliver 1.4-2.4% of total industry revenue in product and R&D, marketing and sales.
Software engineering, too, stands to benefit significantly from AI integration. With the increasing complexity of software development and the demand for faster and more efficient solutions, AI-powered tools can streamline code reviews, automate testing processes and enhance software quality.
Central to AI's capabilities are machine learning solutions, the subset of AI that empowers computers to learn from data and adapt their actions accordingly. As UK IT leaders engage in this dynamic landscape, demystifying the intricacies of machine learning is becoming pivotal in leveraging AI's potential for organisations, enhancing business processes and, ultimately, enriching customer experiences. This understanding is key to unlocking the full potential of Artificial Intelligence (AI) for organisations globally.
Defining AI and machine learning
AI can be succinctly defined as systems displaying behaviours or executing tasks that typically necessitate human intelligence. This includes a spectrum of approaches, from rule-based systems to expert systems, symbolic reasoning and machine learning. At its core, machine learning revolves around trained models, which decipher complex data sets, thereby making informed decisions.
These models evolve through a learning process, primarily classified into three categories: supervised learning, unsupervised learning and reinforcement learning. Here we look at these three fundamental machine learning model training methods:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
In supervised learning, the machine learning model is trained on labelled data, where the desired outputs are already known. The model learns to generalise from this labelled data, enabling it to make predictions or classifications on new, unseen data. This approach is commonly applied in tasks like predicting a value or assigning objects to specific categories.
How it works
1. Input and output pairing: The model is presented with input data from the training dataset, along with their corresponding labels.
2. Prediction calculation: Current model parameters are used to calculate predictions for the given input data.
3. Error calculation: Comparisons are made between the model's predictions and the true labels to determine the extent of discrepancy or error.
4. Update model parameters: The model's internal parameters are adjusted to minimise error. Optimisation techniques like gradient descent are typically used here.
Supervised learning has many applications across industries and use cases. It is particularly useful for solving problems related to prediction, classification and decision-making. Examples include:
1. Email spam classification: Trained on a dataset of labelled emails, a supervised learning model can learn to classify incoming emails as either spam or not spam. This application helps in automating email filtering and reducing the time spent on managing unwanted messages.
2. Medical diagnosis: The model could be trained with patient data with known diagnoses so that it can learn to classify new patient cases based on symptoms and medical history.
3. Customer churn prediction: A supervised learning model can predict the likelihood of customers churning — cancelling their subscriptions or leaving a service — by analysing historical customer data and their churn status. This helps businesses identify at-risk customers and take proactive measures to retain them.
4. Predicting house prices: Historical data on house features and their corresponding sale prices can be used to train a supervised learning model to predict the price of a new house based on features alone.
Unsupervised learning uses unlabelled data to train models to uncover hidden patterns or structures within the data. Models trained with unsupervised learning explore the data’s intrinsic properties and can be used for tasks like clustering or dimensionality reduction. Clustering algorithms, for example, group similar data points together based on their inherent similarities. Dimensionality reduction is another technique which is used to simplify the data by identifying its key features.
How it works
1. Pattern identification: Input data is analysed with the aim being to identify any underlying patterns or relationships.
2. Clustering or dimensionality reduction: Similar data points are organised or grouped together in a process known as clustering. The model can, alternatively, reduce the dimensionality of the data by extracting meaningful features or representations.
3. Feature extraction: new features or representations that capture important characteristics of the data may be extracted. This process facilitates further analysis or downstream tasks.
4. Iteration and refinement: the model will iteratively adjust its parameters to improve its ability to capture and represent the patterns or structures present in the dataset.
Because unsupervised learning operates on unlabelled data it is particularly powerful for uncovering hidden patterns and structures within a dataset. These include:
1. Anomaly detection: An unsupervised learning model can detect anomalies in data and uncover unusual patterns or outliers that may indicate fraudulent activities, system failures or other anomalies.
2. Customer segmentation: Organisations can utilise unsupervised learning to segment their customers based on their purchasing behaviour, preferences or demographic attributes. Clustering algorithms can also be used to group similar customers together. Grouping in this way allows organisations to tailor personalised recommendations for each customer segment.
3. Image and document classification: Unsupervised learning can aid in classifying and organising large collections of images or documents. By applying clustering techniques, the model can group similar images or documents together. This action makes it easier to navigate and search through the data.
4. Dimensionality reduction for feature selection: Unsupervised learning can be used to reduce the dimensionality of high-dimensional datasets while preserving important information. By extracting meaningful features or representations through dimensionality reduction techniques, the model can reduce the complexity of the data, making it more manageable for further analysis and modelling tasks.
Reinforcement learning involves an intelligent agent being trained to make decisions based on feedback. The agent receives feedback through either rewards or penalties based on its actions. Feedback can then be used to improve the models decision-making ability.
How it works
1. Exploration and exploitation: The model explores the environment by taking different actions, often using an exploration strategy like epsilon-greedy or Boltzmann exploration. This allows the model to gather information about the rewards associated with different actions. Based on the learned knowledge, the model exploits the environment by selecting actions that are expected to maximise long-term rewards.
2. Action selection: An action is selected based on its policy function, which could be deterministic or stochastic. The selected action influences the state transition in the environment.
3. Feedback: The agent receives feedback in the form of rewards from the environment based on the action taken. Rewards indicate the immediate desirability or consequences of the agent's actions.
4. Value Update: The model updates its value function based on the observed rewards and the estimation of expected future rewards. Techniques like temporal difference learning, Q-learning or policy gradients are commonly used for value function updates.
Reinforcement learning is particularly useful for making decisions in dynamic environments. These include:
1. Autonomous driving: Reinforcement learning can be used to train agents to make decisions relating to routing. The agents will receive rewards or penalties based on their actions, allowing them to learn and improve their driving capabilities through trial and error.
2. Resource management: Agents can learn to make decisions on resource allocation and consumption to maximise efficiency and minimise waste. They receive rewards or penalties based on the outcomes of their actions, enabling them to learn optimal strategies for sustainable resource usage.
3. Robotics: Agents can learn to perform complex tasks by interacting with their environment. For example, a robot arm can learn to grasp objects with different shapes and sizes by receiving rewards for successful grasps and penalties for failed attempts.
4. Game playing: Reinforcement learning has been successfully employed in training agents to play games, such as chess. The agents learn optimal strategies by exploring different moves and receiving rewards or penalties based on their performance in the game. This allows them to gradually improve their decision-making abilities and compete against human players.
Understanding your AI solutions
Understanding these three key machine learning training methods is pivotal for IT leaders venturing into AI implementation. These methods underpin AI's core capabilities, guiding how machines learn from data, recognise patterns and make decisions. IT leaders equipped with this knowledge can strategically apply these methods to enhance decision-making and tap into AI’s transformative power across industries.
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